machine learning techniques

Use of Machine Learning Techniques for Case-Detection of Varicella Zoster Using Routinely Collected Textual Ambulatory Records: Pilot Observational Study

The purpose of this paper is to compare machine learning techniques in their application to EHR analysis for disease detection. Boosting has demonstrated promising performance in large-scale EHR-based infectious disease identification.

Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?

This study shows that BMLTs perform worse than expected in classifying the presence of EIMs compared to classical statistical tools in a context where mixed genetic and clinical data are available but relevant data are also missing, as often occurs in clinical practice.